Integration of Prediction Uncertainty into a Human Operator Planning Model Realized with Coloured Petri Nets

Abstract

This contribution integrates uncertainty in human performance models based on Coloured Petri Nets to predict human performance more realistically. As decisions often have to be made under uncertainty and existing models of human cognitive performance use if-then rules, the arising questions are about the combination of uncertainty with these rules. Uncertainty is present, if the environment is not exactly known or cannot be predicted precisely. As discrete uncertainty can be well integrated into Petri Nets, but continuous uncertainty is at least of equal importance, this contribution extends Coloured Petri Nets to represent continuous uncertainty as well which was not realized before. First, a probability distribution is selected and subsequently implemented in Coloured Petri Nets. Finally, the integration into an application example is shown. An experiment demonstrated that a planning model including prediction uncertainty is able to describe the performance of human operators during interaction scenarios more realistically.